Data Platform Engineer
@ BrightAI Corporation

Palo Alto, California, United States
$150,000
On Site
Full Time
Posted 23 days ago

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Job Details

About the Role

BrightAI Corporation is a high-growth company transforming business operations with AI, IoT, and cloud-native services. As a Data Platform Engineer, you will build and maintain the data infrastructure powering our products and services.

Responsibilities

  • Design, build, and maintain scalable, high-throughput data ingestion pipelines.
  • Implement robust data lake and SQL-based storage architectures.
  • Develop internal tools using Python, Golang, and SQL.
  • Collaborate with Cloud, Edge, Product, and AI teams.
  • Utilize AWS services including S3, Lambda, Glue, Kinesis, Athena, and RDS.
  • Manage infrastructure via Terraform and adhere to IaC practices.
  • Monitor pipelines using CloudWatch, SumoLogic, or DataDog.
  • Optimize storage, partitioning, and query performance.
  • Participate in architecture reviews ensuring security and compliance.

Skills and Qualifications

5+ years in data or software engineering, strong in Python and Golang, deep SQL proficiency, and hands-on experience with modern data lake architectures. Experience with AWS cloud services, Terraform, scalable ETL/ELT pipelines, and event-driven architectures is essential. Bonus for IoT sensor data expertise and familiarity with analytics tools and Kubernetes.

Key skills/competency

  • Data Ingestion
  • Python
  • Golang
  • SQL
  • AWS
  • Terraform
  • Data Lake
  • ETL/ELT
  • IoT
  • Cloud-native
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How to Get Hired at BrightAI Corporation

🎯 Tips for Getting Hired

  • Research BrightAI Corporation: Review company culture on LinkedIn.
  • Tailor your resume: Highlight data engineering skills.
  • Showcase cloud expertise: Emphasize AWS and Terraform experience.
  • Prepare for technical interviews: Practice coding and system design questions.

📝 Interview Preparation Advice

Technical Preparation

Review AWS service functionalities and limits.
Practice Python and Golang coding exercises.
Study Terraform scripting and IaC best practices.
Prepare design questions on scalable data architecture.

Behavioral Questions

Describe cross-team collaboration experiences.
Discuss problem-solving during data pipeline issues.
Explain handling high-pressure project deadlines.
Share experiences in adapting to evolving tech.

Frequently Asked Questions